Mutual Commit People Matching Process

ABSTRACT

A method and system for an automatic people matching with a mutual commit process is disclosed. The process includes a recommender system that generates people recommendations based, at least in part, on inferences of preferences derived from system usage behaviors. The process also includes variations of a mutual commitment process that may only reveal a first party&#39;s interest in making their expression of interest with a second party if a reciprocal interest in revealing expression of interest is indicated. The process enhances social networking by reducing potential embarrassment and fear of rejection.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims priority under 35 U.S.C. § 119(e) to U.S.Provisional Patent Application Ser. No. 60/823,671, entitled “MutualCommit People Matching Process,” filed Aug. 28, 2006.

FIELD OF THE INVENTION

This invention relates to methods and systems for computer-based peoplerecommendations and matching.

BACKGROUND OF THE INVENTION

People are interested in meeting or connecting with other people tofoster rewarding relationships whether for business, shared interests,or romance.

Prior art online people matching approaches include social networkingsites and dating sites. In some of these prior art processes andsystems, there is a limited degree of automation in the generation ofrecommendations of people that might be of interest to potentially meetonline or offline, or to potentially include in a contact group. Theseautomated recommendations rely on determining the degree to whichinformation within profiles that are explicitly provided by users of thesystem have similarities. This approach is limited by the amount ofinformation that is, or can be, explicitly provided by the respectiveparties, and by the quality and sincerity of the information provided bythe parties.

Further, in prior art online people matching processes, one of theparties has to overtly make contact with, or express interest in, asecond party of interest. There can be an embarrassment factor for oneor both parties that can inhibit such overt and transparent acts ofexpressing an interest in making contact, as a party's overture may berejected. Or the overture may be accepted by the second party, but onlyfor the purposes of not embarrassing the first party. In other words,acceptance may potentially be insincere, which is an uncomfortablesituation for both parties.

These problems with prior art systems and processes both inhibit thedevelopment of contacts and relationships that would be mutuallyrewarding, as well as creating “contact inflation” of “mercy”relationships that have little or no value to one or both of theparties.

SUMMARY OF THE INVENTION

In accordance with the embodiments described herein, a method and systemis disclosed for an automated mutual commit people matching process.

The present invention may apply the adaptive and/or recombinant methodsand systems as described in PCT Patent Application No. PCT/US2004/37176,entitled “Adaptive Recombinant Systems,” filed on Nov. 4, 2004, and mayapply the adaptive and/or recombinant processes, methods, and/or systemsas described in PCT Patent Application No. PCT/US2005/011951, entitled“Adaptive Recombinant Processes”, filed on Apr. 8, 2005.

Other features and embodiments will become apparent from the followingdescription, from the drawings, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow diagram of a mutual commit people recommendationprocess, according to some embodiments;

FIG. 2 is a block diagram of a computer-based mutual commit peoplematching process, according to some embodiments;

FIG. 3 is a diagram of a usage behavior framework, according to someembodiments;

FIG. 4 is a diagram of a user communities and associated relationships,according to some embodiments;

FIG. 5 is a block diagram of a the usage behavior information andinferences function, according to some embodiments; and

FIG. 6 is a diagram of alternative computing topologies of the mutualcommit people matching process, according to some embodiments.

DETAILED DESCRIPTION

In the following description, numerous details are set forth to providean understanding of the present invention. However, it will beunderstood by those skilled in the art that the present invention may bepracticed without these details and that numerous variations ormodifications from the described embodiments may be possible.

In accordance with the embodiments described herein, a method and asystem for an automated mutual commit people matching process isdisclosed to address the shortcomings of prior art people referral,recommendation, and matching processes.

In some embodiments, the people matching method of the present inventionmay constitute an adaptive recommendation or sponsored recommendation asdescribed in PCT Patent Application No. PCT/US2004/37176, entitled“Adaptive Recombinant Systems,” filed on Nov. 4, 2004, or as describedin PCT Patent Application No. PCT/US2005/011951, entitled “AdaptiveRecombinant Processes”, filed on Apr. 8, 2005, which are both herebyincorporated by reference as if set forth in their entirety.

The present invention includes two integrated novelties versus priorart: 1) an automated recommender system is applied to suggest parties ofinterest wherein the recommendation is based, at least in part, oninferences from the behaviors of one or both of the parties, and therecommendation may optionally include an explanation of why therecommendation was made, and 2) a double blind two party commitmentprocess in which a bilateral expression of interest is revealed to theparties if and only if both parties have expressed a unilateral interestin the other. Many variations of this two-step integrated approach maybe applied, as will be discussed in more detail herein.

The present invention has several advantages over prior art peoplematching methods and systems. First, the automated recommender system ofthe present invention introduces a “third party” (the computer-basedrecommender) to the match making; a third party that generatesrecommendations on people of interest based on information and logicthat may be non-obvious to the recommendation recipient. Such anapproach introduces a level of intrigue (because of the non-obviousnessof the logic of the recommendation) and credibility (because inferencesfrom behaviors are more credible than inferences solely fromself-described attributes) that is missing from simple selfdescription-based profile matching. In some embodiments, thecomputer-based recommender system may provide an explanation of why afirst party was recommended to the second party. This may provide anenhanced level of perceived authority associated with therecommendation. Further, the “third party” recommender reducesembarrassment for recommended parties as the parties can “blame” therecommender system for suggesting the parties should connect, thus, atleast in part, removing the onus or responsibility from the partiesthemselves. This enables many more valuable connections to be made thannon-recommender system-based approaches, or with recommender systemsbased solely on information explicitly provided by the parties.

Second, the delivery of a people recommendation is made, in someembodiments of the present invention, so as to shield the expression ofinterest of a first party for a second party unless the second partyalso expresses an interest in the first party. Further, the system maychoose, either by randomization or deterministic means, to notnecessarily deliver bilateral recommendations to two parties, so that aparty is not guaranteed that if they receive a recommendation of asecond party, that the second party will receive a recommendation forthe first party. This method reduces the potential for feelings ofrejection if an expression of interest is not reciprocated, since it isnot guaranteed that both parties received a recommendation for the otherparty.

In some embodiments not all the details (for example, name,organization, contact information) of the parties may be delivered bythe recommender system, to reduce biases, or to reduce the risk of oneof the parties contacting another party that has not expressedreciprocating interest.

FIG. 1 depicts a flow chart of the mutual commit people recommendationprocess, according to some embodiments. A recommendation of a secondparty is delivered to a first party 2010. The recommendation may begenerated by a computer-based system based, at least in part, onbehavioral-based inferences associated with the two parties. Therecommendation may be one among a plurality of other peoplerecommendations and/or recommendations of content (content may include,but is not limited to, web pages, documents, audio, video, andinteractive applications). The recommendation delivery may include anexplanation of why the recommendation was made, including indicatinginferences of relevant or common interests, for expected bilateralinterests, and the explanation may be accessed interactively by therecommendation recipient. The explanation may include reasoning based onbehavioral-based information where the behavioral information may beassociated with one or more usage behavior categories of Table 1.

At the same time, or at a later time, a recommendation of the firstparty may be delivered to the second party 2020. This recommendation mayalso be generated by a computer-based system based, at least in part, onbehavioral-based inferences associated with the two parties.

An expression of interest is detected 2030 for each of the two partiesfor each other. The detection may be through an overt online indicationsuch as, for example, marking a checkbox or any other type of overtlyindicative behavior, or the detection may be based, at least in part, oninferences from non-overt behaviors. In other words, the indication ofinterest in a second party may be explicit and conscious by a firstparty, or it may be inferred by a computer-based system from implicit,unconscious and/or involuntary behaviors or responses of a party. Asjust one example, a physiological response (that is presumablyinvoluntary) may be monitored by the computer-based system, as describedin Table 1, and be used to infer interest in another party. In someembodiments, the expression of interest by one party toward anotherparty, whether implicit or explicitly, may be determined by degree; forexample, from low to high.

The existence of a mutual expression of interest is determined 2040. Ifthere is a mutual expression of interest, or sufficient level ofbilateral interest, then the mutual expression of interests are revealedto both parties 2050. The delivery of the notification of mutualexpression of interest may be through any electronic or computer-basedmeans, including, but not limited to, e-mail, instant messaging, andtelephone. Contact information may be provided to each party so thatthey can make contact with one another. An explanation of why each partywas recommended to the other may be included. The explanation may beinteractive wherein more details are provided as they are requested by aparty.

FIG. 2 represents a summary schematic of a computer-based mutual commitpeople matching process 2002. One or more users 200 interact with, orare monitored by, 915 one or more computer-based systems 925. Theinteractions 915 may be in conjunction with navigating the systems,performing a search, or any other usage behavior, including, but notlimited to, those referenced by the usage behavior categories ofTable 1. The interactions 915 may occur before a recommendation isdelivered, or after a recommendation 910 is delivered to the one or moreusers 200.

Selective usage behaviors 920 associated with the one or more users 200are accessible by the one or more computer based systems 925. The usagebehaviors 920 may occur prior to, or after, the delivery of arecommendation 910 to the one or more users 200.

The one or more computer-based systems 925 include functions to executesome or all of the steps of the mutual commit people recommendationprocess 2001 of FIG. 1. The computer-based mutual commit recommendationprocess 2001 s of the one or more computer-based systems 925 of FIG. 2includes a function to manage usage behavior information and inferenceson user preferences and/or intentions 220, and includes an expression ofinterest detection function 2520. It also may contain functions, notexplicitly shown in FIG. 2 to deliver notification of mutual interests,and to provide explanatory means with regard to people recommendations910.

The one or more computer-based systems 925 deliver peoplerecommendations 910 to the one or more users 200 and/or non-users 265based, at least in part, on inferences of usage behaviors 920. In someembodiments, the one or more computer-based systems 925 may use explicitprofiling information associated with the users/parties to augmentinferences of usage behaviors 920 in delivering people recommendations910.

The one or more computer-based systems may then detect 2520 anyexpressions 915 of mutual interests associated with the recommendations910,265. The expressions 915 of interest may be explicit by the parties,or may constitute computer-based inferences from, at least in part, thebehavior categories and associated behaviors described in Table 1. Ifmutual expressions 915 of interest are detected 2520 by the one or morecomputer-based systems 925, then the mutual interest is revealed to therespective parties by the one or more computer-based systems 925.

User Behavior Categories

In Table 1, a variety of different user behaviors 920, which, may beused by the one or more computer-based applications 925 as a basis forrecommending a first person to a second person. The user behaviors 920may also be assessed by the one or more computer-based applications 925with regard to determining the level of interest in the first person bythe second person after the said recommendation. This expression ofinterest may be inferred from behaviors of the second person with regardto direct representations of the first person, and/or with regard toderivative objects or proxies of the person (such as authored or ownedcontent). The usage behaviors 920 may be associated with the entirecommunity of users, one or more sub-communities, or with individualusers or users of the one of more computer-based applications 925.

It should be emphasized again that the usage behaviors described inTable 1 and the accompanying descriptions may apply to a priori systemsuse 920 (that is, prior to the delivery of a recommendation 910,265) orbehaviors, such as expressions of interest with regard to another party,that is exhibited after receiving a recommendation, where therecommendation may be of another party.

TABLE 1 Usage behavior categories and usage behaviors usage behaviorcategory usage behavior examples navigation and access activity, contentand computer application accesses, including buying/selling paths ofaccesses or click streams execution of searches and/or search historysubscription and personal or community subscriptions to self-profilingprocess topical areas interest and preference self-profiling affiliationself-profiling (e.g., job function) collaborative referral to othersdiscussion forum activity direct communications (voice call, messaging)content contributions or structural alterations reference personal orcommunity storage and tagging personal or community organizing of storedor tagged information direct feedback user ratings of activities,content, computer applications and automatic recommendations usercomments physiological responses direction of gaze brain patterns bloodpressure heart rate environmental conditions current location andlocation location over time relative location to users/object referencescurrent time current weather condition

A first category of process usage behaviors 920 is known as systemnavigation and access behaviors. System navigation and access behaviorsinclude usage behaviors 920 such as accesses to, and interactions withcomputer-based applications and content such as documents, Web pages,images, videos, TV channels, audio, radio channels, multi-media,interactive content, interactive computer applications, e-commerceapplications, or any other type of information item or system “object.”Such content or objects may be representations of people, and mayinclude, such representations of people may include, but are not limitedto, pictures of the person, videos of the person, voice recordings,biographical documents, interests, etc.

These process usage behaviors may be conducted through use of akeyboard, a mouse, oral commands, or using any other input device. Usagebehaviors 920 in the system navigation and access behaviors category mayinclude, but are not limited to, the viewing or reading of displayedinformation, typing written information, interacting with online objectsorally, or combinations of these forms of interactions withcomputer-based applications. This category includes the explicitsearching for information, using, for example, a search engine. Thesearch term may be in the form of a word or phrase to be matched againstdocuments, pictures, web-pages, or any other form of on-line content.Alternatively, the search term may be posed as a question by the user.

System navigation and access behaviors may also include executingtransactions, including commercial transactions, such as the buying orselling of merchandise, services, or financial instruments. Systemnavigation and access behaviors may include not only individual accessesand interactions, but the capture and categorization of sequences ofinformation or system object accesses and interactions over time.

A second category of usage behaviors 920 is known as subscription andself-profiling behaviors. Subscriptions may be associated with specifictopical areas or other elements of the one or more computer-basedsystems 925, or may be associated with any other subset of the one ormore computer-based systems 925. Subscriptions may thus indicate theintensity of interest with regard to elements of the one or morecomputer-based systems 925. The delivery of information to fulfillsubscriptions may occur online, such as through electronic mail (email),on-line newsletters, XML feeds, etc., or through physical delivery ofmedia.

Self-profiling refers to other direct, persistent (unless explicitlychanged by the user) indications explicitly designated by the one ormore users regarding their preferences and/or intentions and interests,or other meaningful attributes. A user 200 may explicitly identifyinterests or affiliations, such as job function, profession, ororganization, and preferences and/or intentions, such as representativeskill level (e.g., novice, business user, advanced). Self-profilingenables the one or more computer-based systems 925 to infer explicitpreferences and/or intentions of the user. For example, a self-profilemay contain information on skill levels or relative proficiency in asubject area, organizational affiliation, or a position held in anorganization. Self profiling may also include information on interestswith regard to meeting other people online or offline. For example, theymay include criteria for location, age, education, gender, physicalfeatures and the like pertaining to people the user may wish to meet orconnect with. A user 200 that is in the role, or potential role, of asupplier or customer may provide relevant context for effective adaptivee-commerce applications through self-profiling. For example, a potentialsupplier may include information on products or services offered in hisor her profile. Self-profiling information may be used to inferpreferences and/or intentions and interests with regard to system useand associated topical areas, and with regard to degree of affinity withother user community subsets. A user may identify preferred methods ofinformation receipt or learning style, such as visual or audio, as wellas relative interest levels in other communities.

A third category of usage behaviors 920 is known as collaborativebehaviors. Collaborative behaviors are interactions among the one ormore users. Collaborative behaviors may thus provide information onareas of interest and intensity of interest. Interactions includingonline referrals of elements or subsets of the one or morecomputer-based systems 925, such as through email, whether to otherusers or to non-users, are types of collaborative behaviors obtained bythe one or more computer-based systems 925.

Other examples of collaborative behaviors include, but are not limitedto, online discussion forum activity, contributions of content or othertypes of objects to the one or more computer-based systems 925, or anyother alterations of the elements, objects or relationships among theelements and objects of one or more computer-based systems 925.Collaborative behaviors may also include general user-to-usercommunications, whether synchronous or asynchronous, such as email,instant messaging, interactive audio communications, and discussionforums, as well as other user-to-user communications that can be trackedby the one or more computer-based systems 925.

A fourth category of process usage behaviors 920 is known as referencebehaviors. Reference behaviors refer to the marking, designating, savingor tagging of specific elements or objects of the one or morecomputer-based systems 925 for reference, recollection or retrieval at asubsequent time. Tagging may include creating one or more symbolicexpressions, such as a word or words, associated with the correspondingelements or objects of the one or more computer-based systems 925 forthe purpose of classifying the elements or objects. The saved or taggedelements or objects may be organized in a manner customizable by users.The referenced elements or objects, as well as the manner in which theyare organized by the one or more users, may provide information oninferred interests of the one or more users and the associated intensityof the interests.

A fifth category of process usage behaviors 920 is known as directfeedback behaviors. Direct feedback behaviors include ratings or otherindications of perceived quality by individuals of specific elements orobjects of the one or more computer-based systems 925, or the attributesassociated with the corresponding elements or objects. The directfeedback behaviors may therefore reveal the explicit preferences and/orintentions of the user. In the one or more computer-based systems 925,the recommendations 910 may be rated by users 200. This enables adirect, adaptive feedback loop, based on explicit preferences and/orintentions specified by the user. Direct feedback also includesuser-written comments and narratives associated with elements or objectsof the computer-based system 925.

A sixth category of process usage behaviors is known as physiologicalresponses. These responses or behaviors are associated with the focus ofattention of users and/or the intensity of the intention, or any otheraspects of the physiological responses of one or more users 200. Forexample, the direction of the visual gaze of one or more users may bedetermined. This behavior can inform inferences associated withpreferences and/or intentions or interests even when no physicalinteraction with the one or more computer-based systems 925 isoccurring. Even more direct assessment of the level of attention may beconducted through access to the brain patterns or signals associatedwith the one or more users. Such patterns of brain functions duringparticipation in a process can inform inferences on the preferencesand/or intentions or interests of users, and the intensity of thepreferences and/or intentions or interests. The brain patterns assessedmay include MRI images, brain wave patterns, relative oxygen use, orrelative blood flow by one or more regions of the brain.

Physiological responses may include any other type of physiologicalresponse of a user 200 that may be relevant for making preference orinterest inferences, independently, or collectively with the other usagebehavior categories. Other physiological responses may include, but arenot limited to, utterances, gestures, movements, or body position.Physiological responses may also include other physical responsephenomena such as, but not limited to, breathing rate, heart rate,temperature, perspiration, blood pressure, or galvanic response.

A seventh category of process usage behaviors is known as environmentalconditions and physical location behaviors. Physical location behaviorsidentify physical location and mobility behaviors of users. The locationof a user may be inferred from, for example, information associated witha Global Positioning System or any other positionally or locationallyaware system or device, or may be inferred directly from locationinformation input by a user (e.g., a zip code or street address), orotherwise acquired by the computer-based systems 925. The physicallocation of physical objects referenced by elements or objects of one ormore computer-based systems 925 may be stored for future reference.Proximity of a user to a second user (including a first person that willbe, or has already been, recommended to a second person), or to physicalobjects referenced by elements or objects of the computer-basedapplication, may be inferred. The length of time, or duration, at whichone or more users reside in a particular location may be used to inferintensity of interests associated with the particular location, orassociated with objects that have a relationship to the physicallocation. Derivative mobility inferences may be made from location andtime data, such as the direction of the user, the speed betweenlocations or the current speed, the likely mode of transportation used,and the like. These derivative mobility inferences may be made inconjunction with geographic contextual information or systems, such asthrough interaction with digital maps or map-based computer systems.Environmental conditions may include the time of day, the weather,lighting levels, sound levels, and any other condition of theenvironment around the one or more users 200.

In addition to the usage behavior categories depicted in Table 1, usagebehaviors may be categorized over time and across user behavioralcategories. Temporal patterns may be associated with each of the usagebehavioral categories. Temporal patterns associated with each of thecategories may be tracked and stored by the one or more computer-basedsystems 925. The temporal patterns may include historical patterns,including how recently an element, object or item of content associatedwith one or more computer-based systems 925. For example, more recentbehaviors may be inferred to indicate more intense current interest thanless recent behaviors.

Another temporal pattern that may be tracked and contribute topreference inferences that are derived, is the duration associated withthe access or interaction with the elements, objects or items of contentof the one or more computer-based systems 925, or the user's physicalproximity to physical objects (including people) referenced by systemobjects of the one or more computer-based systems 925, or the user'sphysical proximity to other users. For example, longer durations maygenerally be inferred to indicate greater interest than short durations.In addition, trends over time of the behavior patterns may be capturedto enable more effective inference of interests and relevancy. Sincedelivered recommendations 910 may include one or more elements, objectsor items of content of the one or more computer-based systems 925, theusage pattern types and preference inferencing may also apply tointeractions of the one or more users with the delivered recommendations910 themselves, including accesses of, or interactions with, explanatoryinformation regarding the logic or rational that the one morecomputer-based systems 925 used in deliver the recommendation 910 to theuser.

User Behavior and Usage Framework

FIG. 3 depicts a usage framework 1000 for performing preference and/orintention inferencing of tracked or monitored usage behaviors 920 by theone or more computer-based systems 925. The usage framework 1000summarizes the manner in which usage patterns are managed within the oneor more computer-based systems 925. Usage behavioral patterns associatedwith an entire community, affinity group, or segment of users 1002 arecaptured by the one or more computer-based systems 925. In another case,usage patterns specific to an individual, shown in FIG. 3 as individualusage patterns 1004, are captured by the one or more computer-basedsystems 925. Various sub-communities of usage associated with users mayalso be defined, as for example “sub-community A” usage patterns 1006,“sub-community B” usage patterns 1008, and “sub-community C” usagepatterns 1010.

Memberships in the communities are not necessarily mutually exclusive,as depicted by the overlaps of the sub-community A usage patterns 1006,sub-community B usage patterns 1008, and sub-community C usage patterns1010 (as well as and the individual usage patterns 1004) in the usageframework 1000. Recall that a community may include a single user ormultiple users. Sub-communities may likewise include one or more users.Thus, the individual usage patterns 1004 in FIG. 3 may also be describedas representing the usage patterns of a community or a sub-community.For the one or more computer-based systems 925, usage behavior patternsmay be segmented among communities and individuals so as to effectivelyenable adaptive advertising delivery 910 for each sub-community orindividual.

The communities identified by the one or more computer-based systems 925may be determined through self-selection, through explicit designationby other users or external administrators (e.g., designation of certainusers as “experts”), or through automatic determination by the one ormore computer-based systems 925. The communities themselves may haverelationships between each other, of multiple types and values. Inaddition, a community may be composed not of human users, or solely ofhuman users, but instead may include one or more other computer-basedsystems, which may have reason to interact with the one or morecomputer-based systems 925. Or, such computer-based systems may providean input into the one or more computer-based systems 925, such as bybeing the output from a search engine. The interacting computer-basedsystem may be another instance of the one or more computer-based systems925.

The usage behaviors 920 included in Table 1 may be categorized by theone or more computer-based systems 925 according to the usage framework1000 of FIG. 3. For example, categories of usage behavior may becaptured and categorized according to the entire community usagepatterns 1002, sub-community usage patterns 1006, and individual usagepatterns 1004. The corresponding usage behavior information may be usedto infer preferences and/or intentions and interests at each of the userlevels.

Multiple usage behavior categories shown in Table 1 may be used by theone or more computer-based systems 925 to make reliable inferences ofthe preferences and/or intentions and/or intentions of a user withregard to elements, objects, or items of content associated with the oneor more computer-based systems 925. There may be different preferenceinferencing results for different users.

By introducing different or additional behavioral characteristics, suchas the duration of access of an item of content, on which to baseupdates to the structure of one or more computer-based systems 925, moreadaptive and relevant people recommendations are enabled. For example,duration of access will generally be much less correlated withnavigational proximity than access sequences will be, and thereforeprovide a better indicator of true user preferences and/or intentionsand/or intentions. Therefore, combining access sequences and accessduration will generally provide better inferences and associated systemstructural updates than using either usage behavior alone. Effectivelyutilizing additional usage behaviors as described above will generallyenable increasingly effective system structural updating. In addition,the one or more computer-based systems 925 may employ user affinitygroups to enable even more effective system structural updating than areavailable merely by applying either individual (personal) usagebehaviors or entire community usage behaviors.

Furthermore, relying on only one or a limited set of usage behavioralcues and signals may more easily enable potential “spoofing” or “gaming”of the one or more computer-based systems 925. “Spoofing” or “gaming”the one or more computer-based systems 925 refers to conductingconsciously insincere or otherwise intentional usage behaviors 920, soas to influence the costs of recommendations 910 of the one or morecomputer-based systems 925. Utilizing broader sets of system usagebehavioral cues and signals may lessen the effects of spoofing orgaming. One or more algorithms may be employed by the one or morecomputer-based systems 925 to detect such contrived usage behaviors, andwhen detected, such behaviors may be compensated for by the preferenceand interest inferencing algorithms of the one or more computer-basedsystems 925.

In some embodiments, the one or more computer-based systems 925 mayprovide users 200 with a means to limit the tracking, storing, orapplication of their usage behaviors 920. A variety of limitationvariables may be selected by the user 200. For example, a user 200 maybe able to limit usage behavior tracking, storing, or application byusage behavior category described in Table 1. Alternatively, or inaddition, the selected limitation may be specified to apply only toparticular user communities or individual users 200. For example, a user200 may restrict the application of the full set of her process usagebehaviors 920 to preference or interest inferences by one or morecomputer-based systems 925 for application to only herself, and make asubset of process behaviors 920 available for application to users onlywithin her workgroup, but allow none of her process usage behaviors tobe applied by the one or more computer-based systems 925 in makinginferences of preferences and/or intentions and/or intentions orinterests for other users.

User Communities

As described above, a user associated with one or more systems 925 maybe a member of one or more communities of interest, or affinity groups,with a potentially varying degree of affinity associated with therespective communities. These affinities may change over time asinterests of the user 200 and communities evolve over time. Theaffinities or relationships among users and communities may becategorized into specific types. An identified user 200 may beconsidered a member of a special sub-community containing only onemember, the member being the identified user. A user can therefore bethought of as just a specific case of the more general notion of user oruser segments, communities, or affinity groups.

FIG. 4 illustrates the affinities among user communities and how theseaffinities may automatically or semi-automatically be updated by the oneor more computer-based systems 925 based on user preferences and/orintentions which are derived from user behaviors 920. An entirecommunity 1050 is depicted in FIG. 4. The community may extend acrossorganizational, functional, or process boundaries. The entire community1050 includes sub-community A 1064, sub-community B 1062, sub-communityC 1069, sub-community D 1065, and sub-community E 1070. A user 1063 whois not part of the entire community 1050 is also featured in FIG. 4.

Sub-community B 1062 is a community that has many relationships oraffinities to other communities. These relationships may be of differenttypes and differing degrees of relevance or affinity. For example, afirst relationship 1066 between sub-community B 1062 and sub-community D1065 may be of one type, and a second relationship 1067 may be of asecond type. (In FIG. 4, the first relationship 1066 is depicted using adouble-pointing arrow, while the second relationship 1067 is depictedusing a unidirectional arrow.)

The relationships 1066 and 1067 may be directionally distinct, and mayhave an indicator of relationship or affinity associated with eachdistinct direction of affinity or relationship. For example, the firstrelationship 1066 has a numerical value 1068, or relationship value, of“0.8.” The relationship value 1068 thus describes the first relationship1066 between sub-community B 1062 and sub-community D 1065 as having avalue of 0.8.

The relationship value may be scaled as in FIG. 4 (e.g., between 0 and1), or may be scaled according to another interval. The relationshipvalues may also be bounded or unbounded, or they may be symbolicallyrepresented (e.g., high, medium, low).

The user 1063, which could be considered a user community including asingle member, may also have a number of relationships to othercommunities, where these relationships are of different types,directions and relevance. From the perspective of the user 1063, theserelationship types may take many different forms. Some relationships maybe automatically formed by the one or more computer-based systems 925,for example, based on interests or geographic location or similartraffic/usage patterns. Thus, for example the entire community 1050 mayinclude users in a particular city. Some relationships may becontext-relative. For example, a community to which the user 1063 has arelationship could be associated with a certain process, and anothercommunity could be related to another process. Thus, sub-community E1070 may be the users associated with a product development business towhich the user 1063 has a relationship 1071; sub-community B 1062 may bethe members of a cross-business innovation process to which the user1063 has a relationship 1073; sub-community D 1065 may be experts in aspecific domain of product development to which the user 1063 has arelationship 1072. The generation of new communities which include theuser 1063 may be based on the inferred interests of the user 1063 orother users within the entire community 1050.

Membership of communities may overlap, as indicated by sub-communities A1064 and C 1069. The overlap may result when one community is wholly asubset of another community, such as between the entire community 1050and sub-community B 1062. More generally, a community overlap will occurwhenever two or more communities contain at least one user or user incommon. Such community subsets may be formed automatically by the one ormore systems 925, based on preference inferencing from user behaviors920. For example, a subset of a community may be formed based on aninference of increased interest or demand of particular content orexpertise of an associated community. The one or more computer-basedsystems 925 is also capable of inferring that a new community isappropriate. The one or more computer-based systems 925 will thus createthe new community automatically.

For each user, whether residing within, say, sub-community A 1064, orresiding outside the community 1050, such as the user 1063, therelationships (such as arrows 1066 or 1067), affinities, or“relationship values” (such as numerical indicator 1068), and directions(of arrows) are unique. Accordingly, some relationships (and specifictypes of relationships) between communities may be unique to each user.Other relationships, affinities, values, and directions may have moregeneral aspects or references that are shared among many users, or amongall users of the one or more computer-based systems 925. A distinct andunique mapping of relationships between users, such as is illustrated inFIG. 4, could thus be produced for each user by the one or morecomputer-based systems 925.

The one or more computer-based systems 925 may automatically generatecommunities, or affinity groups, based on user behaviors 920 andassociated preference inferences. In addition, communities may beidentified by users, such as administrators of the process orsub-process instance 930. Thus, the one or more computer-based systems925 utilizes automatically generated and manually generated communities.

The communities, affinity groups, or user segments aid the one or morecomputer-based systems 925 in matching interests optimally, developinglearning groups, prototyping process designs before adaptation, and manyother uses. For example, some users that use or interact with the one ormore computer-based systems 925 may receive a preview of a newadaptation of a process for testing and fine-tuning, prior to otherusers receiving this change.

The users or communities may be explicitly represented as elements orobjects within the one or more computer-based systems 925.

Preference and/or Intention Inferences

The usage behavior information and inferences function 220 of the one ormore computer-based systems 925 is depicted in the block diagram of FIG.5. Recall from FIG. 2 that the usage behavior information and inferencesfunction 220 tracks or monitor usage behaviors 920 of users 200. Theusage behavior information and inferences function 220 denotes capturedusage information 202, further identified as usage behaviors 270, andusage behavior pre-processing 204. The usage behavior information andinferences function 220 thus reflects the tracking, storing,classification, categorization, and clustering of the use and associatedusage behaviors 920 of the one or more users or users 200 interactingwith the one or more computer-based systems 925.

The captured usage information 202, known also as system usage or systemuse 202, includes any interaction by the one or more users or users 200with the system, or monitored behavior by the one or more users 200. Theone or more computer-based systems 925 may track and store user keystrokes and mouse clicks, for example, as well as the time period inwhich these interactions occurred (e.g., timestamps), as captured usageinformation 202. From this captured usage information 202, the one ormore computer-based systems 925 identifies usage behaviors 270 of theone or more users 200 (e.g., web page access or physical locationchanges of the user). Finally, the usage behavior information andinferences function 220 includes usage-behavior pre-processing, in whichusage behavior categories 246, usage behavior clusters 247, and usagebehavioral patterns 248 are formulated for subsequent processing of theusage behaviors 270 by the one or more computer-based systems 925. Someusage behaviors 270 identified by the one or more computer-based systems925, as well as usage behavior categories 246 designated by the one ormore computer-based systems 925, are listed in Table 1.

The usage behavior categories 246, usage behaviors clusters 247, andusage behavior patterns 248 may be interpreted with respect to a singleuser 200, or to multiple users 200, in which the multiple users may bedescribed herein as a community, an affinity group, or a user segment.These terms are used interchangeably herein. A community is a collectionof one or more users, and may include what is commonly referred to as a“community of interest.” A sub-community is also a collection of one ormore users, in which members of the sub-community include a portion ofthe users in a previously defined community. Communities, affinitygroups, and user segments are described in more detail, below.

Usage behavior categories 246 include types of usage behaviors 270, suchas accesses, referrals to other users, collaboration with other users,and so on. These categories and more are included in Table 1, above.Usage behavior clusters 247 are groupings of one or more usage behaviors270, either within a particular usage behavior category 246 or acrosstwo or more usage categories. The usage behavior pre-processing 204 mayalso determine new “clusterings” of user behaviors 270 in previouslyundefined usage behavior categories 246, across categories, or among newcommunities. Usage behavior patterns 248, also known as “usagebehavioral patterns” or “behavioral patterns,” are also groupings ofusage behaviors 270 across usage behavior categories 246. Usage behaviorpatterns 248 are generated from one or more filtered clusters ofcaptured usage information 202.

The usage behavior patterns 248 may also capture and organize capturedusage information 202 to retain temporal information associated withusage behaviors 270. Such temporal information may include the durationor timing of the usage behaviors 270, such as those associated withreading or writing of written or graphical material, oralcommunications, including listening and talking, or physical location ofthe user 200, potentially including environmental aspects of thephysical location(s). The usage behavioral patterns 248 may includesegmentations and categorizations of usage behaviors 270 correspondingto a single user of the one or more users 200 or according to multipleusers 200 (e.g., communities or affinity groups). The communities oraffinity groups may be previously established, or may be generatedduring usage behavior pre-processing 204 based on inferred usagebehavior affinities or clustering. Usage behaviors 270 may also bederived from the use or explicit preferences and/or intentions 252associated with other systems.

Computing Infrastructure

FIG. 6 depicts various computer hardware and network topologies that thecomputer-based mutual commit people matching process 2002 may embody.

Servers 950, 952, and 954 are shown, perhaps residing at differentphysical locations, and potentially belonging to different organizationsor individuals. A standard PC workstation 956 is connected to the serverin a contemporary fashion, potentially through the Internet. It shouldbe understood that the workstation 956 can represent any computer-baseddevice, mobile or fixed, including a set-top box. In this instance, therelevant systems, in part or as a whole, may reside on the server 950,but may be accessed by the workstation 956. A terminal or display-onlydevice 958 and a workstation setup 960 are also shown. The PCworkstation 956 or servers 950 may be connected to a portable processingdevice (not shown), such as a mobile telephony device, which may be amobile phone or a personal digital assistant (PDA). The mobile telephonydevice or PDA may, in turn, be connected to another wireless device suchas a telephone or a GPS receiver.

FIG. 6 also features a network of wireless or other portable devices962. The relevant systems may reside, in part or as a whole, on all ofthe devices 962, periodically or continuously communicating with thecentral server 952, as required. A workstation 964 connected in apeer-to-peer fashion with a plurality of other computers is also shown.In this computing topology, the relevant systems, as a whole or in part,may reside on each of the peer computers 964.

Computing system 966 represents a PC or other computing system, whichconnects through a gateway or other host in order to access the server952 on which the relevant systems, in part or as a whole, reside. Anappliance 968, includes software “hardwired” into a physical device, ormay utilize software running on another system that does not itself hostthe relevant systems. The appliance 968 is able to access a computingsystem that hosts an instance of one of the relevant systems, such asthe server 952, and is able to interact with the instance of the system.

While the present invention has been described with respect to a limitednumber of embodiments, those skilled in the art will appreciate numerousmodifications and variations therefrom. It is intended that the appendedclaims cover all such modifications and variations as fall within thescope of this present invention.

1. A computer-based people matching method comprising: determining the expected level of interest by a first party for a second party based, at least in part, on a plurality of behaviors corresponding to a plurality of usage behavior categories; determining the expected level of interest by the second party for the first party based, at least in part, on a plurality of behaviors corresponding to a plurality of usage behavior categories; determining if the level of mutual interest expressed by the parties is sufficient to reveal the expression of mutual interest to the parties; and revealing to the parties the expression of mutual interest.
 2. The method of claim 1 further comprising: delivering a recommendation to the first party of the second party.
 3. The method of claim 2 further comprising: delivering an explanation to the first party of why the second party was recommended to the first party comprising, at least in part, of behavioral-based information.
 4. The method of claim 1 wherein determining the expected level interest by a first party for a second party based, at least in part, on a plurality of behaviors corresponding to a plurality of usage behavior categories comprises: evaluating a behavior corresponding to a physiological responses usage behavior category.
 5. The method of claim 1 wherein determining the expected level interest by a first party for a second party based, at least in part, on a plurality of behaviors corresponding to a plurality of usage behavior categories comprises: evaluating a behavior corresponding to an environmental conditions and location usage behavior category.
 6. The method of claim 1 wherein determining if the level of mutual interest expressed by the parties is sufficient to reveal the expression of mutual interest to the parties comprises: randomizing recommendations wherein a recommendation of a second party to a first party does not guarantee a recommendation of the first party to the second party.
 7. The method of claim 1 wherein determining if the level of mutual interest expressed by the parties is sufficient to reveal the expression of mutual interest to the parties comprises: determining the parties' mutual commitment in revealing the mutual interest to each party.
 8. A computer-based people matching system comprising: a function to determine the expected level of interest by a first party for a second party based, at least in part, on a plurality of behaviors corresponding to a plurality of usage behavior categories; a function to determine the expected level of interest by the second party for the first party based, at least in part, on a plurality of behaviors corresponding to a plurality of usage behavior categories; a function to determine if the level of mutual interest expressed by the parties is sufficient to reveal the expression of mutual interest to the parties; and a function to reveal to the parties the expression of mutual interest.
 9. The system of claim 8 further comprising: a function to deliver a recommendation to the first party of the second party.
 10. The system of claim 9 further comprising: a function to deliver an explanation to the first party of why the second party was recommended to the first party comprising, at least in part, of behavioral-based information.
 11. The system of claim 8 wherein a function to determine the expected level interest by a first party for a second party based, at least in part, on a plurality of behaviors corresponding to a plurality of usage behavior categories comprises: a behavior corresponding to a physiological responses usage behavior category.
 12. The system of claim 8 wherein a function to determine the expected level interest by a first party for a second party based, at least in part, on a plurality of behaviors corresponding to a plurality of usage behavior categories comprises: a behavior corresponding to an environmental conditions and location usage behavior category.
 13. The system of claim 8 wherein a function to determine if the level of mutual interest expressed by the parties is sufficient to reveal the expression of mutual interest to the parties comprises: a function to randomize recommendations wherein a recommendation of a second party to a first party does not guarantee a recommendation of the first party to the second party.
 14. The system of claim 8 wherein a function to determine if the level of mutual interest expressed by the parties is sufficient to reveal the expression of mutual interest to the parties comprises: a function to determine the parties' mutual commitment to reveal the mutual interest to each party.
 15. A computer-based people matching system comprising: a people recommendations function comprising means to generate people recommendations based, at least in part, on a plurality of behaviors corresponding to a plurality of usage behavior categories; a people matching function comprising means to determine by evaluating usage behaviors if the level of mutual interest expressed by reciprocally recommended people recommended by the people recommendations function is sufficient to reveal the expression interest of mutual interest to the parties; and a function to reveal to the reciprocally recommended people the expression of mutual interest.
 16. The system of claim 15 wherein a people recommendations function comprising means to generate people recommendations based, at least in part, on a plurality of behaviors corresponding to a plurality of usage behavior categories comprises: a function to generate an explanation of why a first person was recommended to a second person, comprising, at least in part, behavioral-based information.
 17. The system of claim 15 wherein a people matching function comprising means to determine by evaluating mutual usage behaviors if the level of mutual interest expressed by reciprocally recommended people recommended by the people recommendations function is sufficient to reveal the expression of mutual interest to the parties comprises: a function to evaluate physiological responses of the reciprocally recommended people. 